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Targeted next generation sequencing identifies two novel mutations in SEPN1 in rigid spine muscular dystrophy 1.

Oncotarget | 2016

Rigid spine muscular dystrophy 1 (RSMD1) is a neuromuscular disorder, manifested with poor axial muscle strength, scoliosis and neck weakness, and a variable degree of spinal rigidity with an early ventilatory insufficiency which can lead to death by respiratory failure. Mutations of SEPN1 gene are associated with autosomal recessive RSMD1. Here, we present a clinical molecular study of a Chinese proband with RSMD1. The proband is a 17 years old male, showing difficulty in feeding, delayed motor response, problem in running with frequent fall down, early onset respiratory insufficiency, general muscle weakness and rigid cervical spine. Muscle biopsy identified increased variability of fiber size with atrophic muscle cells consistent with non-specific myopathic changes. Proband's elder brother presented with same phenotype as the proband and died at the age of 15 years due to acute respiratory failure. Proband's father and mother are phenotypically normal. Targeted exome capture based next generation sequencing and Sanger sequencing identified that the proband was a compound heterozygote with two novel mutations in SEPN1 gene; a novel missense mutation (c.1384T>C; p.Sec462Arg) and a novel nonsense mutation (c.1525C>T; p.Gln509Ter), inherited from his father and mother respectively. These two mutations are co-segregated with the disease phenotypes in the proband and was absent in normal healthy controls. Our present study expands the mutational spectrum of the SEPN1 associated RSMD1.

Pubmed ID: 27863379 RIS Download

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1000 Genomes Project and AWS (tool)

RRID:SCR_008801

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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SOAPsnp (tool)

RRID:SCR_010602

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software providng a method based on Bayes? theorem (the reverse probability model) to call consensus genotype by carefully considering the data quality, alignment, and recurring experimental errors.

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